Assessment of site characteristics as predictors of the vulnerability of Norway spruce (<i>Picea abies</i> Karst.) stands to attack by <i>Ips typographus</i> L. (Col., Scolytidae)
Bibliographic record
Abstract
The intensity of bark beetle Ips typographus L. (Col., Scolytidae) attack on Norway spruce (Picea abies Karst.) is known to vary greatly among stands. In a control strategy approach, previous studies investigated the relationships between the variability in intensity of I. typographus attack and site characteristics such as stand age and altitude, mean tree circumference, growth rate and nearest‐neighbour distance, soil moisture, pH in H2O and KCl, and soil contents of C, N, K, P, Mg, Ca, Fe, Cu, Zn and Mn. The data analysis method used in these studies was mainly the multiple linear regression, with the mean number of attacks per spruce tree in a stand as variable to explain. Previous results showed that the expected vulnerability of a Norway spruce stand to attack by I. typographus can be estimated on the basis of simple information of easy access to the forester, when the data on the stand in question is used with others for fitting the regression model. Prediction of the vulnerability of a stand, without including its data in the fitting of the model, was shown to be more approximate. Therefore, the objectives of this study were: (1) to improve the performance of models predicting the vulnerability of Norway spruce stands to attack by I. typographus, based on site characteristics; (2) to assess the stability of such predictive models when these are built using a moderate number of stands; and (3) to incorporate the resulting information in a global approach to control and prevention. Published data were re‐analysed for these purposes. A jackknifed multiple linear regression procedure, in which each stand in turn is discarded when fitting the model (jackknife replication), is presented. A great variability in the models fitted, depending on the stand discarded, is observed. For instance, the number of explanatory variables retained ranges from one (i.e. soil P content, for five jackknife replications) to 10 (for one jackknife replication), for R2‐values ranging from 0.5 to 1.0 and for one influential stand (i.e. the same stand characterized by an atypically low number of insect attacks compared to other stands with similar soil P content) against many influential stands. Differences between the model finally selected here using the revisited data and the models proposed earlier are discussed. A path analysis diagram is proposed for a more comprehensive modelling of Norway spruce stand vulnerability to I. typographus attack, based on site characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".